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Studying Cancer Individuality by Personal and Predictive Drug Screening and Differential OMICs

Periodic Reporting for period 2 - SCIPER (Studying Cancer Individuality by Personal and Predictive Drug Screening and Differential OMICs)

Reporting period: 2020-05-01 to 2021-10-31

In the ERC-StG “SCIPER” project, we study the molecular and cellular processes that influence how cancer patients respond to their medication. We do so by combining image-based drug screening in patient biopsies (a technique we call “Pharmacoscopy”), with molecular profiling of those same biopsies and patient data, in a series of three clinical studies. The three clinical studies are designed to push the development of our Pharmacoscopy technology from blood cancers (in an multiple myeloma study), to fluid biopsies of solid tumors, and into a fully solid tumor study, thus ensuring we broaden the technical applicability of the technology.

A better understanding of the molecular and cellular systems that drive people’s response to their medication, combined with the development of cellular assays that better predict how patients will respond to their treatments, has direct benefit for both patients and society as a whole. Patient lives may be prolonged, unwanted side effects may be avoided, the drug design and discovery process may be improved, drug approval trials may become more successful from more tailored patient inclusion, and society as a whole can save significant health years and care-associated costs.

The study includes the following objectives:
1. Integrate image-based ex vivo drug screening with differential OMICs from tumour biopsies, across patient cohorts and at least two cancer indications.
a. Using patient-internal cancer- vs. healthy-cell comparisons in both the drug screening and OMICs.
b. Turning the very patient-to-patient variability that complicates patient treatment into a statistical signal that we can mine for causal inference.
2. Develop single-round multiplexed immunofluorescence assays and convolutional neural network-based machine learning to simultaneously record drug responses of cancer cells, immune cells, healthy cell, their cellular interactions, and their cellular states and morphologies.
3. Infer causal molecule→phenotype relationships to identify the molecular determinants that govern drug response individuality in cancer, validate top-down and selected hypotheses, and publicly share the resource.

With as Overall Goals SCIPER project defined as follows:
1. Identify the regulatory principles governing drug response variability among cancer patients.
2. Understand and accurately predict treatment responses of late-stage cancer patients.

With regards to our progress: Objectives #1a, #1b, and #2 are fully achieved in the context of both our MM and fluid biopsy studies. Objective #3 is currently ongoing on the data collected over the past 2.5 years. Overall Goals #1 and #2 are thus already now achieved, although the results are yet to be published, and will be expanded in the remaining of the project.
We have initiated the three clinical studies, and generated the majority of experimental data and techniques planned in the context of the project. The second period of this ERC-StG project will focus on data analysis, dissemination, publication, and exploitation.
The ongoing clinical studies and developed technologies of the SCIPER project are all beyond the state-of-the-art, no equivalent studies are reported online. Expected results until the end of the project includes the completion of the goals and objectives as set out in the original proposal.